class_docstr: Create image patches from a torch Tensor or numpy array.
  This transform was proposed in Jigsaw - https://arxiv.org/abs/1603.09246
  
  Args:
      num_patches (int): how many image patches to create
      patch_jitter (int): space to leave between patches
docstr: Instantiates ImgPatchesFromTensor from configuration.
  
  Args:
      config (Dict): arguments for for the transform
  
  Returns:
      ImgPatchesFromTensor instance.
docstr_tok: ["Instantiates","ImgPatchesFromTensor","from","configuration",".","Args",":","config","(","Dict",")",":","arguments","for","for","the","transform","Returns",":","ImgPatchesFromTensor","instance","."]
code: def from_config(
      cls, config: Dict[str, Any]
  ) -> "ImgPatchesFromTensor":
      """
      Instantiates ImgPatchesFromTensor from configuration.
  
      Args:
          config (Dict): arguments for for the transform
  
      Returns:
          ImgPatchesFromTensor instance.
      """
      num_patches = config.get("num_patches", 9)
      patch_jitter = config.get("patch_jitter", 21)
      logging.info(
          f"ImgPatchesFromTensor | Using num_patches: {num_patches}"
      )
      logging.info(
          f"ImgPatchesFromTensor | Using patch_jitter: {patch_jitter}"
      )
      return cls(num_patches=num_patches, patch_jitter=patch_jitter)
  
file_path: vissl/vissl/data/ssl_transforms/img_patches_tensor.py
imports: import logging
  import math
  from typing import Any, Dict
  import numpy
  from classy_vision.dataset.transforms import register_transform
  from classy_vision.dataset.transforms.classy_transform import ClassyTransform
label_desc: Use image node_id 3 for calling the ImgPatchesFromTensor obj's underlying member method code with example usage: obj.from_config(cls, config) and returns: cls